Since the coefficients in the regression represent key parameters such as elasticities and cross-elasticities of demand, it is important that we test their statistical significance.
The t-test is used for this purpose. For each coefficient, it tests that the coefficient differs significantly from zero value.
Consider the regression model: $$ y = b_0+b_1 x_1+b_2 x_2+b_3 x_3 \,…$$
For the coefficient b1, in the above equation:
Null hypothesis H0: b1 = 0
Alternative hypothesis HA: b1 <> 0
Relevant test statistic is t = b1 /σb1, where σb1 is the standard error of b1.
Relevant distribution is the t-distribution, with degrees of freedom = n − (k + 1). Where k is the number of explanatory variables, and n is sample size.
Similar tests are run for b2, b3 … to determine the individual significance of X2 and X3 in the model.
Use the Search Bar to find content on MarketingMind.
In an analytics-driven business environment, this analytics-centred consumer marketing workshop is tailored to the needs of consumer analysts, marketing researchers, brand managers, category managers and seasoned marketing and retailing professionals.
Is marketing education fluffy too?
Marketing simulators impart much needed combat experiences, equipping practitioners with the skills to succeed in the consumer market battleground. They combine theory with practice, linking the classroom with the consumer marketplace.